Various systems have been developed to provide location, velocity, and/or attitude information of an object to a user. Inertial based navigation systems (INS) that track changes in location and attitude from an initial point are capable of providing location, velocity, and/or attitude information that is accurate and low in noise. Inertial navigation systems (INS) use inertial measurement units (IMU) to create navigation solutions for objects, including vehicles and human mounted objects. IMUs measure acceleration and rotation rates. Processor can integrate the acceleration and rotation rates determined by an IMU over time to estimate the location and attitude of an object. As an object travels for an extended period of time, errors in the measurements may arise and accumulate, causing the estimated location and attitude of the object to “drift” from the object's actual location and attitude. To correct for these errors, external systems, such as global navigation satellite systems (GNSS) (for example, global positioning system (GPS)), are capable of providing low drift information, but the data tends to be noisy and subject to radio propagation error such as multipath.
Light detection and ranging (LIDAR) sensors and systems are able to generate representations of a surrounding environment. LIDAR systems are able to produce large quantities of data and enable detailed representations of environments. The large quantities of data can be difficult to manage and can take long times to manipulate. The data regarding a current representation of an environment can be correlated against historical data representing a larger area to determine where in the larger area the LIDAR system currently resides. One particular problem with the large quantities of data produced by LIDAR systems is that is it time consuming to determine a current location by correlating data regarding the current location generated by the LIDAR with a historical map. It is particularly time consuming to initialize the system to find the current location initially. Once the currently location is initially located, it does not take as much time to incrementally update based on the LIDAR data.